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Target Recognition based on Fusing Features of Visible and Two Wave Bands Infrared Images

机译:基于可见光和两波段红外图像融合特征的目标识别

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摘要

Single source images do not contain the comprehensive information of the target and the target recognition rate is low. Targeting to these problems, a new target recognition method based on fusing features of the target in Visible (VIS), Middle Wave Infrared (MWIR) and Long Wave Infrared (LWIR) images is proposed. First, typical features of the target in VIS, MWIR and LWIR images are extracted. The fused feature vector is subsequently obtained by feature selection based on Fisher discriminant analysis. Finally, target recognition is realized through Support Vector Machine (SVM). The target recognition performance of the proposed method and five other target recognition algorithms were tested and compared on VIS, MWIR and LWIR images. Experimental results illustrate that the target recognition rate of the proposed algorithm is higher than that of the five other algorithms tested. The proposed algorithm also has high operational efficiency and robustness. (C) 2019 Society for Imaging Science and Technology.
机译:单源图像不包含目标的全面信息,目标识别率低。针对这些问题,提出了一种基于目标在可见光(VIS),中波红外(MWIR)和长波红外(LWIR)图像中融合特征的新目标识别方法。首先,在VIS,MWIR和LWIR图像中提取目标的典型特征。随后通过基于Fisher判别分析的特征选择获得融合的特征向量。最后,通过支持向量机(SVM)实现目标识别。测试了该方法的目标识别性能以及其他五种目标识别算法,并在VIS,MWIR和LWIR图像上进行了比较。实验结果表明,该算法的目标识别率高于其他五种算法。该算法还具有较高的运算效率和鲁棒性。 (C)2019影像科学与技术学会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2019年第1期|010503.1-010503.11|共11页
  • 作者单位

    Sci & Technol Avion Integrat Lab, Shanghai 200233, Peoples R China|China Natl Aeronaut Radio Elect Res Inst, Shanghai 200233, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Astronaut, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Astronaut, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Astronaut, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Astronaut, Nanjing 210016, Jiangsu, Peoples R China;

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  • 入库时间 2022-08-18 04:13:31

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